NDLI: Discrimination of three or more partial discharge sources by multi-step clustering of cumulative energy features

Content Provider	IET Digital Library
Author	Zhu, Ming Xiao Wang, Yan Bo Chang, Ding Ge Zhang, Guan Jun Shao, Xian Jun Zhan, Jiang Yang Chen, Ji Ming
Abstract	Partial discharge (PD)-based diagnosis is extensively employed in condition assessment of electrical equipment. In the case of multiple PD sources, discrimination of mixed signals is significant for reliable PD interpretation. To improve the separation performance of three or more PD sources, a multi-step discrimination method is proposed. The cumulative energy functions are exploited to characterise wave shapes of PD signals, and width and sharpness features are extracted and classified to separate mixed patterns. With the objective of maximising a novel evaluation parameter of separation capability, the oblique line and length of structure element are optimised in the feature extraction stage. For the multi-step discrimination method, the feature extraction and clustering procedures are repeatedly applied to the whole dataset, sub-classes, sub-sub-classes etc., until no more clusters are generated. To evaluate the separation performance of the proposed algorithm, a mathematical model for PD pulse is proposed, which is the multiplication of Heidler enveloping function and an oscillating function whose frequency spectrum confirms Gaussian distribution. In the end, the multi-step discrimination method is tested with PD current pulses and ultra-high-frequency signals of three artificial defects in transformer and gas-insulated system, and the results prove the effectiveness of the proposed algorithm.
Starting Page	149
Ending Page	159
Page Count	11
ISSN	17518822
Volume Number	13
e-ISSN	17518830
Issue Number	Issue 2, Mar (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-smt/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-smt.2018.5240
Journal	IET Science, Measurement & Technology
Publisher Date	2018-10-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Artificial Defects Clustering Procedures Condition Assessment Cumulative Energy Features Cumulative Energy Function Dielectric Breakdown And Discharges Electrical Equipment Feature Extraction Feature Extraction Stage Frequency Spectrum Gas Insulated Transformer Gas-insulated System Gaseous Insulation, Breakdown And Discharges Gaussian Distribution Heidler Enveloping Function Mathematical Model Mixed Signal Multiple PD Source Multistep Clustering Multistep Discrimination Method Oscillating Function Partial Discharge Source Partial Discharge-based Diagnosis Partial Discharges Pattern Clustering PD Current Pulses PD Signal Reliable PD Interpretation Separation Capability Separation Performance Sharpness Feature Structure Element Transformer Transformer And Reactors Ultrahigh-frequency Signal Width Feature
Content Type	Text
Resource Type	Article
Subject	Atomic and Molecular Physics, and Optics Electrical and Electronic Engineering

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Blind Source Separation of Mixed PD Signals Produced by Multiple Insulation Defects in GIS

Propogation characteristics of PD-induced UHF signal in 126 kV GIS with three-phase construction based on time–frequency analysis

Discrimination of three or more partial discharge sources by multi-step clustering of cumulative energy features

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